CNNs have been the focus of machine learning. AlexNet [1]} first proved the high robustness of the CNN in visual classification. VGG [2]} and Inception [3]} further proved that the depth of CNNs is a crucial factor affecting performance by stacking convolutional layers. However, simply stacking the convolutional layers beyond a certain limit may degrade the network. Contrary to stacking, ResNet [4]} proved that skip connections can significantly improve performance without increasing the number of layers and other parameters, and laid an important foundation for the development of CNNs. Based on ResNet, WideReNet [5]} proved that increasing the network width can also enhance the network's performance. Different from WideReNet [5]}, ResNeXt [7]} enriches the convolution styles by adding different convolution groups, i.e., cardinality, so as to increase the diversity of features. Most recent deep network architecture designs target how to broaden a factor, such as depth, width, and cardinality, which increases many parameters.
